#include <caffe/caffe.hpp>
#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif  // USE_OPENCV
#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>

using namespace caffe;  // NOLINT(build/namespaces)
using std::string;

/* Pair (label, confidence) representing a prediction. */
typedef std::pair<string, float> Prediction;

class Classifier {
 public:
  Classifier(const string& model_file,
             const string& trained_file,
             const string& mean_file,
             const string& label_file);

  std::vector<Prediction> Classify(const cv::Mat& img, int N = 2);

 private:
  void SetMean(const string& mean_file);

  std::vector<float> Predict(const cv::Mat& img);

  void WrapInputLayer(std::vector<cv::Mat>* input_channels);

  void Preprocess(const cv::Mat& img,
                  std::vector<cv::Mat>* input_channels);

 private:
  shared_ptr<Net<float> > net_;
  cv::Size input_geometry_;
  int num_channels_;
  cv::Mat mean_;
  std::vector<string> labels_;
};

Classifier::Classifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file) {

  Caffe::set_mode(Caffe::GPU);


  /* Load the network. */
  net_.reset(new Net<float>(model_file, TEST));
  net_->CopyTrainedLayersFrom(trained_file);


  Blob<float>* input_layer = net_->input_blobs()[0];
  num_channels_ = input_layer->channels();
 /* CHECK(num_channels_ == 3 || num_channels_ == 1)
    << "Input layer should have 1 or 3 channels.";*/
  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());

  /* Load the binaryproto mean file. */
  SetMean(mean_file);

  /* Load labels. */
  std::ifstream labels(label_file.c_str());
 /* CHECK(labels) << "Unable to open labels file " << label_file;*/
  string line;
  while (std::getline(labels, line))
    labels_.push_back(string(line));

  Blob<float>* output_layer = net_->output_blobs()[0];
 /* CHECK_EQ(labels_.size(), output_layer->channels())
    << "Number of labels is different from the output layer dimension.";*/
}

static bool PairCompare(const std::pair<float, int>& lhs,
                        const std::pair<float, int>& rhs) {
  return lhs.first > rhs.first;
}

/* Return the indices of the top N values of vector v. */
static std::vector<int> Argmax(const std::vector<float>& v, int N) {
  std::vector<std::pair<float, int> > pairs;
  for (size_t i = 0; i < v.size(); ++i)
    pairs.push_back(std::make_pair(v[i], static_cast<int>(i)));
  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);

  std::vector<int> result;
  for (int i = 0; i < N; ++i)
    result.push_back(pairs[i].second);
  return result;
}

/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
 
  std::vector<float> output = Predict(img);

  N = std::min<int>(labels_.size(), N);
  std::vector<int> maxN = Argmax(output, N);
  std::vector<Prediction> predictions;
  for (int i = 0; i < N; ++i) {
    int idx = maxN[i];
    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
  }

  return predictions;
}

/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {
  BlobProto blob_proto;
  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

  /* Convert from BlobProto to Blob<float> */
  Blob<float> mean_blob;
  mean_blob.FromProto(blob_proto);
 /* CHECK_EQ(mean_blob.channels(), num_channels_)
    << "Number of channels of mean file doesn't match input layer.";*/

  /* The format of the mean file is planar 32-bit float BGR or grayscale. */
  std::vector<cv::Mat> channels;
  float* data = mean_blob.mutable_cpu_data();
  for (int i = 0; i < num_channels_; ++i) {
    /* Extract an individual channel. */
    cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
    channels.push_back(channel);
    data += mean_blob.height() * mean_blob.width();
  }

  /* Merge the separate channels into a single image. */
  cv::Mat mean;
  cv::merge(channels, mean);

  /* Compute the global mean pixel value and create a mean image
   * filled with this value. */
  cv::Scalar channel_mean = cv::mean(mean);
  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
}

std::vector<float> Classifier::Predict(const cv::Mat& img) {
  Blob<float>* input_layer = net_->input_blobs()[0];
  input_layer->Reshape(1, num_channels_,
                       input_geometry_.height, input_geometry_.width);
  /* Forward dimension change to all layers. */
  net_->Reshape();

  std::vector<cv::Mat> input_channels;
  WrapInputLayer(&input_channels);

  Preprocess(img, &input_channels);

  net_->Forward();

  /* Copy the output layer to a std::vector */
  Blob<float>* output_layer = net_->output_blobs()[0];
  const float* begin = output_layer->cpu_data();
  const float* end = begin + output_layer->channels();
  return std::vector<float>(begin, end);
}

/* Wrap the input layer of the network in separate cv::Mat objects
 * (one per channel). This way we save one memcpy operation and we
 * don't need to rely on cudaMemcpy2D. The last preprocessing
 * operation will write the separate channels directly to the input
 * layer. */
void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
  Blob<float>* input_layer = net_->input_blobs()[0];

  int width = input_layer->width();
  int height = input_layer->height();
  float* input_data = input_layer->mutable_cpu_data();
  for (int i = 0; i < input_layer->channels(); ++i) {
    cv::Mat channel(height, width, CV_32FC1, input_data);
    input_channels->push_back(channel);
    input_data += width * height;
  }
}

void Classifier::Preprocess(const cv::Mat& img,
                            std::vector<cv::Mat>* input_channels) {
  /* Convert the input image to the input image format of the network. */
  cv::Mat sample;
  if (img.channels() == 3 && num_channels_ == 1)
    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
  else if (img.channels() == 4 && num_channels_ == 1)
    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
  else if (img.channels() == 4 && num_channels_ == 3)
    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
  else if (img.channels() == 1 && num_channels_ == 3)
    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
  else
    sample = img;

  cv::Mat sample_resized;
  if (sample.size() != input_geometry_)
    cv::resize(sample, sample_resized, input_geometry_);
  else
    sample_resized = sample;

  cv::Mat sample_float;
  if (num_channels_ == 3)
    sample_resized.convertTo(sample_float, CV_32FC3);
  else
    sample_resized.convertTo(sample_float, CV_32FC1);

  cv::Mat sample_normalized;
  cv::subtract(sample_float, mean_, sample_normalized);

  /* This operation will write the separate BGR planes directly to the
   * input layer of the network because it is wrapped by the cv::Mat
   * objects in input_channels. */
  cv::split(sample_normalized, *input_channels);

  /*CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
        == net_->input_blobs()[0]->cpu_data())
    << "Input channels are not wrapping the input layer of the network.";*/
}

//std::vector<bool> scratch_find(std::vector<cv::Mat> images){
//
//  string model_file   = "E:/Projects/caffe/windows/caffe-windows/caffe/models/bvlc_reference_caffenet/deploy.prototxt";
//  string trained_file = "E:/Projects/caffe/windows/caffe-windows/logs/caffenet/caffenet_train_iter_100000.caffemodel";
//  string mean_file    = "D:/caffetrain1/lmdb-leveldb/mean.binaryproto";
//  string label_file   = "D:/caffetrain1/lmdb-leveldb/synset_words.txt";
//  Classifier classifier(model_file, trained_file, mean_file, label_file);
//  std::vector<bool> a;
//  for (int i = 0; i < images.size(); ++i)
//  {
//	  std::vector<Prediction> predictions = classifier.Classify(images[i]);
//	  /* Print the top N predictions. */
//		  Prediction p = predictions[0];
//		  if (p.first == "0 normal")
//		  {
//			  a.push_back(true);
//		  }
//		  else
//		  {
//			  a.push_back(false);
//		  }
//  }
//  
//  return a;
//
//}

std::vector<std::vector<bool>> scratch_find(std::vector<std::vector<cv::Mat>> images) {

	::google::InitGoogleLogging("scratch");
	//::google::ShutdownGoogleLogging();
	string model_file = "E:/Projects/caffe/windows/caffe-windows/caffe/models/bvlc_reference_caffenet/deploy.prototxt";
	string trained_file = "E:/Projects/caffe/windows/caffe-windows/logs/caffenet/caffenet_train_iter_100000.caffemodel";
	//string model_file = "E:/Projects/caffe/windows/caffe-windows/logs/caffenew-3.19-nice/deploy.prototxt";
	//string trained_file = "E:/Projects/caffe/windows/caffe-windows/logs/caffenew-3.19-nice/caffenet_train_iter_100000.caffemodel";
	string mean_file = "D:/caffetrain1/lmdb-leveldb/mean.binaryproto";
	string label_file = "D:/caffetrain1/lmdb-leveldb/synset_words.txt";
	Classifier classifier(model_file, trained_file, mean_file, label_file);
	std::vector<std::vector<bool>> a;
	for (int j = 0; j < images.size(); j++)
	{
		std::vector<bool> b;
		for (int i = 0; i < images[j].size(); ++i)
		{
			std::vector<Prediction> predictions = classifier.Classify(images[j][i]);
			/* Print the top N predictions. */
			Prediction p = predictions[0];
			if (p.first == "0 normal")
			{
				b.push_back(true);
			}
			else
			{
				b.push_back(false);
			}
		}
		a.push_back(b);
	}

	return a;

}

void cutImage(cv::Mat src, int m, int n, int x, int y, cv::Rect& Roi, cv::Mat &dst)
{
	Roi.width = m;
	Roi.height = n;
	Roi.x = x;
	Roi.y = y;
	dst = src(Roi);
}


std::vector<std::vector<cv::Rect>>CheckScratch(std::vector<cv::Mat> beDetectImg)
{
	std::vector<std::vector<cv::Mat>> InfoImage;
	std::vector<std::vector<cv::Rect>> InfoRect;
	for (int m = 0; m < beDetectImg.size(); m++)
	{
		int size_width = 200;
		int size_height = 200;
		int index_x = 0;
		int index_y = 0;
		int x_max = cvCeil(beDetectImg[m].cols / size_width);
		int y_max = cvFloor(beDetectImg[m].rows / size_height);
		std::vector<cv::Mat> vecImgTemp;
		std::vector<cv::Rect> vecRectTemp;
		for (int i = 0; i <= x_max; i++)
		{
			if (i < x_max)
			{
				index_x = i * size_width;
			}
			else
			{
				index_x = beDetectImg[m].cols - size_width;
			}
			for (int j = 0; j <= y_max; j++)
			{
				if (j < y_max)
				{
					index_y = j * size_height;
				}
				else
				{
					index_y = beDetectImg[m].rows - size_height;
				}
				cv::Rect Roi = {};
				cv::Mat Image_cut;
				cutImage(beDetectImg[m], size_width, size_height, index_x, index_y, Roi, Image_cut);
				vecImgTemp.push_back(Image_cut);
				vecRectTemp.push_back(Roi);

			}
		}
		InfoImage.push_back(vecImgTemp);
		InfoRect.push_back(vecRectTemp);
	}
	std::vector<std::vector<bool>>res = {};
	res = scratch_find(InfoImage);
	std::vector<std::vector<cv::Rect>> resRect;
	for (int m = 0; m < res.size(); m++)
	{
		std::vector<cv::Rect> resRectTemp;
		for (int n = 0; n < res[m].size(); n++)
		{
			if (res[m][n] == false)
			{
				resRectTemp.push_back(InfoRect[m][n]);
			}
		}
		resRect.push_back(resRectTemp);
	}

	return resRect;

}



//std::vector<cv::Rect>CheckScratch(cv::Mat beDetectImg)
//{
//	int size_width = 200;
//	int size_height = 200;
//	int index_x = 0;
//	int index_y = 0;
//	std::vector<cv::Rect> scratch_clt;
//	std::vector<cv::Mat>smallImages;
//	int x_max = cvCeil(beDetectImg.cols / size_width);
//	int y_max = cvFloor(beDetectImg.rows / size_height);
//	for (int i = 0; i <= x_max; i++)
//	{
//		if (i < x_max)
//		{
//			index_x = i * size_width;
//		}
//		else
//		{
//			index_x = beDetectImg.cols - size_width;
//		}
//		for (int j = 0; j <= y_max; j++)
//		{
//			if (j < y_max)
//			{
//				index_y = j * size_height;
//			}
//			else
//			{
//				index_y = beDetectImg.rows - size_height;
//			}
//			cv::Rect Roi = {};
//			cv::Mat Image_cut;
//			cutImage(beDetectImg, size_width, size_height, index_x, index_y, Roi, Image_cut);
//			smallImages.push_back(Image_cut);
//			scratch_clt.push_back(Roi);
//		}
//	}
//	std::vector<bool>res;
//	std::vector<cv::Rect>resRect = {};
//	res = scratch_find(smallImages);
//	for (int i = 0; i < res.size(); i++)
//	{
//		if (res[i] == false)
//		{
//			resRect.push_back(scratch_clt[i]);
//		}
//	}
//	return resRect;
//}
//int main()
//{
//	cv::Mat src = cv::imread("C:/Users/liwenjun/Desktop/a8.tiff", 0);
//	std::vector<cv::Rect> d;
//	time_t begin = clock();
//	d = CheckScratch(src);
//	std::cout << "time:" << (double)(clock() - begin) / CLOCKS_PER_SEC << std::endl;
//	time_t begin1 = clock();
//	cv::Mat dst(src.size(), CV_8UC3, cv::Scalar::all(0));
//	cv::cvtColor(src, dst, CV_GRAY2BGR);
//	for (int k = 0; k < d.size(); k++)
//	{
//		cv::rectangle(dst, cvPoint(d[k].x, d[k].y), cvPoint(d[k].x + d[k].width, d[k].y + d[k].height), cvScalar(0, 0, 255), 3, 4, 0);
//	}
//
//	cv::resize(dst, dst, cv::Size(648, 500));
//	cv::resize(src, src, cv::Size(648, 500));
//	imshow("detectImage", dst);
//	imshow("src", src);
//	cvWaitKey(0);
//	system("pause");
//	return 0;
//
//}

//int main()
//{
//	cv::Mat src = cv::imread("C:/Users/liwenjun/Desktop/a23.tiff", 0);
//	cv::Mat src1 = cv::imread("C:/Users/liwenjun/Desktop/a8.tiff", 0);
//	std::vector<cv::Mat> src2;
//	src2.push_back(src);
//	src2.push_back(src1);
//	std::vector<std::vector<cv::Rect>> d;
//	time_t begin = clock();
//	d = CheckScratch(src2);
//	std::cout << "time:" << (double)(clock() - begin) / CLOCKS_PER_SEC << std::endl;
//	time_t begin1 = clock();
//	std::vector<cv::Mat> dstRes;
//	for (int m = 0; m < src2.size(); m++)
//	{
//		cv::Mat dst(src2[m].size(), CV_8UC3, cv::Scalar::all(0));
//		cv::cvtColor(src2[m], dst, CV_GRAY2BGR);
//		for (int n = 0; n < d[m].size(); n++)
//		{
//
//			cv::rectangle(dst, cvPoint(d[m][n].x, d[m][n].y), cvPoint(d[m][n].x + d[m][n].width, d[m][n].y + d[m][n].height), cvScalar(0, 0, 255), 3, 4, 0);
//		}
//		dstRes.push_back(dst);
//	}
//	cv::resize(dstRes[1], dstRes[1], cv::Size(648, 500));
//	imshow("detectImage", dstRes[1]);
//	cvWaitKey(0);
//	system("pause");
//	return 0;
//
//}

int main()
{

	cv::String pattern = "C:/Users/liwenjun/Desktop/1/*.tiff";
	vector<cv::String> fn;
	std::vector<cv::Mat> src2;
	glob(pattern, fn, false);
	size_t count = fn.size();
	char file_dst[100] = "";
	for (int i = 0; i<count; ++i)
	{
		cv::Mat dst;
		dst = imread(fn[i], 0);
		src2.push_back(dst);
	}
	std::vector<std::vector<cv::Rect>> d;
	time_t begin = clock();
	d = CheckScratch(src2);
	std::cout << "time:" << (double)(clock() - begin) / CLOCKS_PER_SEC << std::endl;
	time_t begin1 = clock();
	std::vector<cv::Mat> dstRes;
	for (int m = 0; m < src2.size(); m++)
	{
		cv::Mat dst(src2[m].size(), CV_8UC3, cv::Scalar::all(0));
		cv::cvtColor(src2[m], dst, CV_GRAY2BGR);
		for (int n = 0; n < d[m].size(); n++)
		{

			cv::rectangle(dst, cvPoint(d[m][n].x, d[m][n].y), cvPoint(d[m][n].x + d[m][n].width, d[m][n].y + d[m][n].height), cvScalar(0, 0, 255), 3, 4, 0);
		}
		dstRes.push_back(dst);
	}
	for (int i = 0; i < dstRes.size(); i++)
	{
		sprintf(file_dst, "C:/Users/liwenjun/Desktop/2/test_%d.tiff", (i + 0));
		imwrite(file_dst, dstRes[i]);
	}
	//cv::resize(dstRes[1], dstRes[1], cv::Size(648, 500));
	//imshow("detectImage", dstRes[1]);
	//cvWaitKey(0);
	//system("pause");
	return 0;

}